Search for question
Question

Module Title: Module Code: Academic Year / Semester: ASSESSMENT BRIEF Al Fundamentals KV4004 2023-24/Semester 1 % Weighting (to overall module): 50% Assessment Title: Date of Handout to Students: Mechanism for Handout: Microsoft Azure practical solution to selected Al topic 9th October 2023 Module Blackboard Site & Online session Mechanism for Submission: Submission Format / Word Count A report that presents the practical work done in the first assessment, the implementation approach and a discussion of the findings. The word limit for the report is 1000 words, not including the front cover, table of contents page, references and appendices. Mechanism for return of Feedback and Marks: Mark and individual written feedback will be uploaded to the Module Site on Blackboard. For further queries please email module tutor. LEARNING OUTCOMES The learning outcomes (LOS) for this module are: - Knowledge & Understanding MLO1 Demonstrate knowledge, and basic understanding, of essential facts, concepts, principles, theories, techniques, and technologies related to computing, computer science, data science and Artificial Intelligence (AI) workloads. MLO2 Describe Al workloads and considerations, fundamental principles, features of AI workloads and their implementation on Azure. MLO3 Specify, design and construct simple computer, AI and data-based systems. Intellectual / Professional skills & abilities MLO4 Critically Specify, design and construct simple computer- and AI- based systems (IPSA). Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA) MLO5 Demonstrate a basic awareness of the global, ethical, and cultural issues related to computing, and specifically AI and data - and its societal implications for equality and diversity. This assessment addresses learning outcomes MLO3, MLO4 and ML05. Instructions to students: This is an individual piece of work, and you must not work with others to construct your work. During the semester there are numerous opportunities to seek and get advice and support on your work, from tutors and peers but you must ensure you do not do work for others or copy work from others. Submission Requirements You must comply to the following criteria to fulfil the assignment submission requirements: The word limit for the report is 1500. However, if the assignment is within +10% (i.e., up to 150 words) then NO penalty will be applied. The word count should be declared on the cover page of your assignment. The word count does not include title page, table of contents page, references and appendices and in text citations [e.g. (O'Brien, 2020)]. Academic Conduct: You must adhere to the university regulations on academic conduct. Formal inquiry proceedings will be investigated if there is any suspicion of misconduct or plagiarism in your work. Refer to the University's regulations on assessment if you are unclear as to the meaning of these terms. The latest copy is available on the university website. If you need an extension: Contact ask4Help. Tutors and Module tutors cannot grant extensions. Make sure that your report is submitted on time. University regulations state that assignments submitted late without approval will incur a 10% reduction for the first 24hours then a zero mark after this. You may apply for an extension of time to complete assessed coursework if there are personal circumstances which are unforeseen and unpreventable and have a serious effect on your ability to submit the work by the published hand-in deadline. You must submit an 'Application for Authorisation for Late Submission of Assessed Work' before the hand-in deadline. Appropriate medical certification, or other relevant evidence confirming the circumstances, must be provided. Information regarding this policy and procedure can be accessed through your student portal. Disabled students Contact the module lead tutor about reasonable adjustments. Errors If any errors are found in this document, changes will be posted to the eLP (Blackboard). Versions will be clearly stated. All versions will be accepted. INSTRUCTION OF ASSESSMENTS Assessment Brief Microsoft Azure is a cloud computing platform. It provides a wide range of services, including the virtual machines, databases, analytics, AI and more. It offers a robust set of services and tools for implementing AI and machine learning solutions across various domains. It can be used to build, deploy and manage applications and services through Microsoft's global network of data centres. There are various applications of AI in Azure which use Azure services and tools. These Azure services and tools extend to a wide range of applications, from image recognition to natural language understanding to predictive analytics and recommendation systems. You are required to use azure machine learning that realises the practical solution to facial recognition discussed in the first assignment. You are required to provide the details of the implementation approach and a discussion of the findings. You are required to conduct data preparation/transformation to make the data ready for the model. The components you must complete are: 1. Exploration of the facial recognition dataset you have chosen in the previous assignment such as Data preparation/transformation and some visualisations for the data pre-processed [25 Marks] 2. The implementation of the AI including the design, choose the models, training and validation [30 Marks] 3. The implementation of the inference model and testing the inference model by some unseen examples [20 Marks] 4. Discussion of the findings from the AI model built i.e., the performance metrics and their visualisations [25 Marks] Grade 70-100 % APPENDIX A Marking criteria Criteria A mark of 70% or over is indicative of excellent work where the student has more than met the requirements of the assessment brief and demonstrated an exceptional understanding of Al systems on Azure and techniques along with knowledge of their chosen dataset and provides a comprehensive critical view of the workflow of these Al models and excellent presentation of the results by distinctive visualisations. 60-69 60 % 40-59 % 3 30-39 % A mark within this range is highly competent and completed to a high standard. The work demonstrates a good level of understanding of Al services on Azure along with knowledge of their chosen dataset and provides a comprehensive critical view of the workflow of these models and good presentation of the results by visualisations. The requirements of the assessment brief have been met to a high standard but with room for a few minor areas of improvement. Marks at the lower end in this band suggest that students have met all or most of the requirements of the assessment brief but there are a larger number of minor areas needing improvement. A mark within the range indicates a pass, where the work has been completed to a satisfactory standard, but where there is still significant scope for improvement. The work demonstrates an acceptable understanding of Al services on Azure and techniques along with a reasonable knowledge of their chosen dataset and provides a reasonably well-documented account of the workflow of these models. The work will have covered most of the key assessment criteria, but these might be at a more superficial level compared with work in the higher mark ranges, with evidence of a less complete understanding of the subject area. The work may indicate that less independent learning has been performed or that less robust methods are used. This indicates a fail mark, where learning outcomes may not all have been met to a satisfactory standard and where there may be a range of omissions, poor communication and/or possibly a lack of knowledge derived from wider reading. The work does not demonstrate an acceptable understanding of AI services on Azure, nor provides a well-documented account of workflow of these models. Work in this mark range indicates insufficient evidence of